Yasin Abbasi-Yadkori

Sublinear regret in linear bandits is achievable even with strict sliding-window constraints by leveraging a novel "transition diameter" and a rare-switching algorithm, outperforming naive approaches with significantly fewer policy updates.

You can have your cake and eat it too: this new algorithm nearly matches the optimal performance for stochastic best-arm identification while remaining robust to adversarial attacks, despite the theoretical impossibility of a universally optimal learner.